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A decision support system for predicting settling velocity of spherical and non-spherical particles in Newtonian fluids.

Authors :
Rushd, Sayeed
Rahman, Moklesur
Arifuzzaman, Mohammad
Aktaruzzaman, Md
Source :
Particulate Science & Technology; 2022, Vol. 40 Issue 5, p609-619, 11p
Publication Year :
2022

Abstract

An artificial intelligence-based system was developed to efficiently predict settling velocity (SV) using a large dataset comprised of 2726 samples. The ranges of particle size and fluid viscosity were 0.212 − 98.59 mm and 0.02 − 92800 mPa.s, respectively. Properties of particle and fluid were fed to a model as the inputs to obtain SV as the output. Six machine learning algorithms were tested for the prediction. The random forest (RF) performed better than other algorithms with a coefficient of determination of 0.98 and a mean square error of 0.0027. A simple decision support system was developed using the RF model. The current study demonstrates the complete methodology of modeling SV with ML. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02726351
Volume :
40
Issue :
5
Database :
Complementary Index
Journal :
Particulate Science & Technology
Publication Type :
Academic Journal
Accession number :
157383290
Full Text :
https://doi.org/10.1080/02726351.2021.1982092